- Taiwan's Artificial Intelligence Basic Act was promulgated and enacted on January 14, 2026[1], making it Taiwan's first AI-specific legislation. It adopts a principle-based legislation framework, distinct from the global AI regulatory detailed-rule approach, preserving flexibility for subsequent subsidiary legislation and industry guidelines
- The Act establishes four fundamental principles — human-centric, sustainable development, effective governance, and reasonable accountability[2] — and designates the National Science and Technology Council (NSTC) as the central competent authority responsible for coordinating cross-ministerial AI policy
- High-risk AI system classification guidelines are expected to be issued by the Ministry of Digital Affairs (MODA) in Q1 2026[8], covering key application domains including HR recruitment, credit scoring, medical diagnosis, judicial assistance, and autonomous driving. Enterprises should immediately begin AI system audits
- Enterprises face dual compliance requirements under both the Personal Data Protection Act and the AI Basic Act, necessitating a comprehensive compliance framework encompassing transparency labeling, data governance, human oversight, impact assessment, and grievance mechanisms. The financial[9] and healthcare industries will be the first to be affected
1. Legislative Background and Purpose
On January 14, 2026, Taiwan officially entered the era of AI legislation. The promulgation of the Artificial Intelligence Basic Act[1] marks Taiwan's establishment of its position in the global AI governance landscape. From the Executive Yuan's submission to the Legislative Yuan's third reading, the Act underwent multiple cross-party negotiations and industry-academia public hearings, ultimately taking form as a "Basic Act" — a legislative level whose selection carries deep significance: it both declares the national policy direction for AI development and preserves ample room for subsequent ministerial-level subsidiary legislation.
From an international perspective, Taiwan's legislative timing coincides with a critical turning point in global AI regulation. The EU AI Act[5] was formally enacted in 2024 with phased implementation, the OECD continues to update its AI Principles[7], and the United States advances AI governance through a combination of executive orders and industry self-regulation. Taiwan has chosen a distinctive path: adopting principle-based legislation, rather than the EU-style rule-based regulation. This means the Act itself does not enumerate prohibited items or specific technical standards article by article, but instead establishes fundamental principles and governance frameworks, with industry-specific guidelines to be developed by respective competent authorities according to sector characteristics[3].
1.1 Legislative Purpose and Policy Positioning
The legislative purpose of the AI Basic Act can be summarized as a three-fold mission. First, building a foundation of trust. By legally establishing fundamental principles for AI development and application, creating a predictable legal environment for industrial development while addressing societal concerns about AI risks. Second, promoting industrial development. The Act explicitly requires the government to actively promote AI R&D, talent cultivation, and industrial applications[6], avoiding excessive regulation that would stifle innovation. Third, international alignment. Under the global AI governance trend, Taiwan needs an AI-specific law to interface with international standards, particularly for securing a basis for international mutual recognition in areas such as cross-border data flows and AI product export compliance.
1.2 Fundamental Differences from the EU AI Act
The most fundamental difference between Taiwan's AI Basic Act and the EU AI Act lies in their legislative philosophy. The EU AI Act employs a highly structured risk classification system, clearly distinguishing "unacceptable risk," "high risk," "limited risk," and "minimal risk" into four levels, with detailed technical and procedural requirements imposed on high-risk AI systems[5]. Taiwan instead chose to establish a principled framework through a Basic Act, delegating specific classification and compliance requirements to subsequent subsidiary legislation and administrative guidelines. This choice reflects the pragmatism of Taiwan's legislators: AI technology evolves rapidly, and overly detailed provisions may quickly become obsolete. Principle-based legislation preserves greater adaptive flexibility[4].
2. Core Provisions Analysis
The structural framework of the AI Basic Act can be divided into five major pillars: definitions and scope, fundamental principles, government obligations, industrial development, and rights protection. Below is an analysis of the provisions most relevant to enterprises.
2.1 Legal Definition of "Artificial Intelligence"
The Act adopts a functional description for the definition of "artificial intelligence," encompassing systems that are based on technologies such as machine learning and deep learning and are capable of generating predictions, recommendations, decisions, or content for specific purposes[1]. This definition is intentionally kept broad to avoid excluding future emerging forms of AI due to technical terminology limitations. Notably, this definition is highly consistent with the OECD's definition of AI systems[7], facilitating international regulatory alignment. For enterprises, this means that not only applications using large language models like ChatGPT and Claude are regulated by the Act, but traditional machine learning models (such as credit scoring and recommendation systems) also fall within the Act's scope.
2.2 Four Fundamental Principles
The four fundamental principles established by the Act constitute the core framework for enterprise AI compliance[2]:
Human-Centric: AI development and application should have the enhancement of human well-being as its ultimate goal, respecting human dignity and fundamental rights. When deploying AI systems, enterprises must ensure that humans retain ultimate decision-making authority in critical decision-making processes, particularly in scenarios involving individual rights (such as personnel decisions, credit assessments, medical recommendations).
Sustainable Development: AI development should balance economic growth, social equity, and environmental protection. This principle requires enterprises to consider not only operational efficiency gains when evaluating AI investment returns, but also AI's impact on the labor market, social equity, and environmental resources.
Effective Governance: The development, deployment, and use of AI systems should establish appropriate governance mechanisms, including risk assessment, transparency requirements, human oversight, and continuous monitoring. This principle provides the legal authorization basis for subsequent industry-specific guidelines by various ministries.
Reasonable Accountability: AI system developers, deployers, and users should each bear reasonable duty of care and responsibility. When an AI system causes harm, there should be clear attribution principles and remedy channels. Notably, the Act uses the word "reasonable" to qualify accountability — this differs from the EU AI Act's strict liability approach, giving enterprises more room for defense[3].
2.3 More Detailed Value Guidelines
Under the four principles, the Act further articulates multiple operational value guidelines, including: Safety and reliability — AI systems should undergo thorough testing to ensure robustness; Privacy protection — AI data processing should comply with the Personal Data Protection Act; Transparency and explainability — AI decision-making logic should be understandable to a reasonable extent; Fairness and non-discrimination — AI should not produce systemic unfairness against specific groups; Accountability — AI system operations should have complete records for post-hoc tracking[10]. Although these value guidelines are not mandatory provisions with direct penalties, they will serve as the basis for ministerial industry regulations and guidelines. Enterprises should treat them as leading indicators for compliance.
2.4 Government Obligations and Competent Authorities
The Act designates the National Science and Technology Council (NSTC) as the central competent authority, responsible for coordinating cross-ministerial AI policy[6]. Each sectoral competent authority (such as the FSC, Ministry of Health and Welfare, Ministry of Economic Affairs, and MODA) is responsible for developing AI application guidelines and oversight for their respective jurisdictions. Government obligations encompass: promoting AI basic research, fostering AI talent development, establishing AI evaluation and verification mechanisms, developing AI standards and norms, and promoting AI industrial applications and international cooperation. This means enterprises can expect government support in the following areas: AI technology R&D subsidies (through NSTC), AI talent training programs, AI industry sandboxes (led by MODA), and AI international compliance consulting resources.
2.5 Enterprise Self-Regulation and Industry Self-Regulation
The Act encourages enterprises to establish AI self-regulatory guidelines and supports industry associations or industry alliances in developing industry-level AI self-regulatory codes[2]. This design draws from the experiences of Japan and Singapore — establishing best practices through industry self-regulation before formal regulations are introduced, then gradually incorporating mature self-regulatory norms into formal legislation. For enterprises, actively participating in the development of industry self-regulatory norms is not only a forward-looking compliance strategy but also a strategic move to ensure their interests are considered during the standard-setting process.
3. High-Risk AI System Classification
Although the AI Basic Act itself does not directly enumerate a specific list of high-risk AI systems, it authorizes each sectoral competent authority to classify and manage AI applications according to risk levels. MODA is expected to issue high-risk AI classification guidelines in Q1 2026[8], which will be a critical reference document for enterprise compliance.
3.1 AI Applications Likely to Be Classified as High-Risk
Referencing the EU AI Act's high-risk classification framework[5] and Taiwan's existing industry regulatory practices, the following AI application domains are highly likely to be included in the high-risk category:
HR Recruitment and Workforce Management AI: Including automated resume screening, interview assessment, performance prediction, and promotion recommendation systems. These systems directly affect individual employment rights, and if hidden biases exist (such as systemic discrimination against specific genders, ages, or educational backgrounds), they will trigger serious legal and social controversies.
Credit Scoring and Financial Decision AI: Covering automated credit ratings, loan approvals, insurance pricing, and investment advisory systems. The FSC has already issued Core Principles for Financial Industry AI Utilization[9], requiring financial institutions to ensure AI model fairness, transparency, and explainability. The AI Basic Act will provide stronger legal force for these principles.
Medical Diagnosis and Clinical Decision Support AI: Including medical imaging AI interpretation (such as X-ray, CT, MRI), AI-assisted medication recommendations, and pathology analysis systems. Errors in such systems can directly endanger patient safety, and the Ministry of Health and Welfare will inevitably place them under the highest level of regulatory scrutiny.
Judicial Assistance AI: Covering AI-assisted sentencing recommendations, case risk assessment, and legal document analysis systems. These systems involve personal liberty and judicial justice, and their fairness and explainability requirements will be extremely stringent.
Autonomous Driving and Transportation Safety AI: Including autonomous driving systems (all levels), intelligent traffic management, and fleet dispatch AI. The Ministry of Transportation is already drafting autonomous vehicle regulations, and the AI Basic Act will provide the overarching legal basis.
3.2 Risk Classification Management Framework
Taiwan's risk classification framework is expected to reference but not completely replicate the EU AI Act's four-tier model. Based on current policy signals and industry-academia discussions, Taiwan may adopt a three-tier classification: high risk (requiring compliance assessment and continuous monitoring), medium risk (requiring transparency obligations and self-assessment), and general risk (encouraging self-regulation, no additional mandatory requirements)[4]. Unlike the EU AI Act, Taiwan has not explicitly established a "prohibited" category — consistent with the principle-based legislation philosophy, avoiding an overly rigid prohibited list that loses applicability amid rapid technological iteration. However, individual sectoral authorities (such as the NCC and FSC) can still impose restrictions on specific AI applications through industry regulations.
4. Enterprise Compliance Checklist
Based on the Act's fundamental principles and anticipated regulatory direction, enterprises should establish compliance verification mechanisms across six major dimensions. This checklist also considers the existing requirements of the Personal Data Protection Act, as compliance requirements under the personal data law and the AI Basic Act are often overlapping and complementary in AI application scenarios.
4.1 AI System Inventory and Risk Assessment
The first step toward compliance is a comprehensive inventory of all AI systems within the enterprise. Many enterprises discover after conducting an inventory that AI usage extends far beyond management's awareness — from the marketing department's recommendation engines and HR's automated screening tools to the R&D department's code generation assistants, AI has already permeated every business unit. The inventory should cover: system name and purpose description, AI technology type (rule engine, machine learning, deep learning, generative AI), data sources and processing methods, decision impact subjects and scope, and current risk control measures. After completing the inventory, conduct a risk level assessment for each AI system based on the forthcoming risk classification guidelines.
4.2 Transparency Requirements
The "transparency and explainability" principle will translate into specific transparency obligations[2]. At minimum, enterprises should meet the following requirements: AI usage notification — when users interact with an AI system (such as a customer service chatbot), they should be clearly informed that they are interacting with AI rather than a human; AI-generated content labeling — text, images, audio/video, and other content generated by AI should be clearly labeled to prevent users from mistaking it for human creation; Decision explanation — when an AI system makes decisions affecting individual rights (such as credit approval, insurance claims), it should be able to provide an explanation of the decision basis, enabling the affected party to understand why the decision was made.
4.3 Dual Compliance in Data Governance
The enactment of the AI Basic Act subjects enterprises to dual data governance requirements under both the Personal Data Protection Act and the AI law. The Personal Data Protection Act requires enterprises to comply with legality, specific purpose, and proportionality principles when collecting, processing, and using personal data; the AI Basic Act further requires training data quality, representativeness, and bias-free properties. Specifically, enterprises need to ensure: training data acquisition has a lawful authorization basis, dataset diversity is sufficient to avoid systemic bias, sensitive attributes (such as gender, ethnicity, disability status) are appropriately handled in model training, and data retention and destruction comply with the time limits specified by the Personal Data Protection Act.
4.4 Human Oversight Mechanisms
One of the core manifestations of the "human-centric" principle is ensuring that humans maintain appropriate oversight and intervention capability over AI systems[10]. Enterprises should establish: Human-AI collaboration processes — in high-risk decision scenarios, AI serves only as an assistive tool with final decisions made by qualified humans; Emergency shutdown mechanisms — when AI systems exhibit abnormal behavior or produce harmful outputs, clear shutdown procedures and authorization levels should be in place; Objection handling channels — individuals affected by AI decisions should have the right to request human review.
4.5 AI Impact Assessment
Drawing on the concept of Data Protection Impact Assessment (DPIA), enterprises should conduct AI impact assessments before deploying high-risk AI systems[3]. Assessment content should cover: potential impact of the AI system on individual fundamental rights, differential impact on specific groups (such as vulnerable groups, ethnic minorities), possible failure scenarios and their severity, implemented risk mitigation measures and their effectiveness, and residual risk acceptability assessment. The AI impact assessment is not a one-time document but should be periodically updated throughout the AI system's lifecycle — especially when models are updated, data sources change, or application scenarios expand.
4.6 Grievance and Remedy Channels
The "reasonable accountability" principle requires enterprises to establish accessible grievance channels for individuals affected by AI decisions. This includes: clearly publicizing grievance pathways and receiving offices, setting reasonable response timelines, having personnel with decision-making authority handle grievances, maintaining complete records of grievance handling, and conducting root cause analysis on systemic issues to feed back into AI system improvements.
| Compliance Dimension | Core Requirement | Specific Action Items | Priority |
|---|---|---|---|
| AI System Inventory | Fully understand enterprise AI usage | Establish AI system registry covering all departments' AI tools | Highest |
| Risk Classification | Classify and manage by risk level | Conduct risk assessment for each AI system per MODA guidelines | Highest |
| Transparency Labeling | AI usage notification and content labeling | Add notification mechanisms to AI interaction interfaces, label AI-generated content | High |
| Data Governance | Personal Data Act + AI Basic Act dual compliance | Review training data legality, diversity, and bias risk | High |
| Human Oversight | Ensure human oversight capability over AI | Establish human-AI collaboration SOPs, emergency shutdown mechanisms | High |
| Impact Assessment | Complete AI impact assessment for high-risk systems | Develop AI Impact Assessment templates and execution processes | Medium-High |
| Grievance Mechanism | Provide remedy channels for affected parties | Set up grievance offices, define response timelines and handling procedures | Medium |
| Training | Enhance organizational AI literacy and compliance awareness | Conduct compliance training for management and AI users | Medium |
| Record Keeping | Complete AI decision and governance records | Establish AI system logs, audit trails, and document management systems | Medium |
5. Industry Impact Analysis
The impact of the AI Basic Act varies significantly across industries. Below is an in-depth analysis of five key sectors to help enterprises understand the specific compliance challenges they face.
5.1 Financial Industry: The Front Line of Compliance Pressure
The financial industry is undoubtedly one of the sectors most deeply affected by the AI Basic Act. The FSC issued the Core Principles and Related Policies for Financial Industry AI Utilization as early as 2024[9], covering five principles: reliability and safety, fairness and human-centricity, privacy protection and data governance, transparency and explainability, and accountability. The enactment of the AI Basic Act will provide overarching legal force for these principles, elevating them from "self-regulatory guidelines" to "statutory obligations." AI systems used by financial institutions for credit scoring, automated underwriting, robo-advisory, and anti-money laundering detection will almost entirely likely fall into the high-risk classification. Enterprises must pay particular attention to model explainability requirements — when a customer's loan application is rejected by an AI system, financial institutions must be able to provide specific, understandable reasons for the rejection.
5.2 Healthcare Industry: Balancing Safety and Innovation
Healthcare AI regulation involves life safety, and compliance standards will be the strictest. AI-assisted medical image interpretation (such as chest X-ray, skin lesion identification, fundus photography analysis) already has several providers in Taiwan that have obtained TFDA medical device approval. The AI Basic Act will layer additional governance requirements on top of the existing Medical Devices Act. Enterprises should note: AI diagnostic recommendations must be clearly labeled as "reference assistance" rather than "definitive diagnosis"; clinical decision support system algorithm logic must be clearly explained to physicians; patient consent mechanisms for training data must simultaneously comply with both the Personal Data Protection Act and medical regulations; AI medication recommendation systems require pharmacist human review mechanisms.
5.3 Manufacturing Industry: New Dimensions of Quality and Safety
Manufacturing AI applications (such as AOI automated optical inspection, predictive maintenance, quality prediction), while mostly not directly affecting individual rights, may still be classified as high-risk for AI systems involving product safety and industrial safety. For example, an AI-driven safety monitoring system that fails to detect dangerous conditions in time could lead to workplace accidents; missed detections by an AI quality inspection system could allow defective products to reach the market. Manufacturing enterprises should particularly focus on AI system reliability verification — does AI model performance remain stable under harsh industrial conditions of high temperature, high humidity, and vibration? Does the system make erroneous judgments when sensor data is abnormal?
5.4 Human Resources: A Sensitive Zone for Bias Risk
AI applications in human resources (automated resume screening, video interview analysis, performance prediction, employee attrition prediction) represent the field where fairness controversies are most concentrated. Biases in historical recruitment data (such as certain positions historically being predominantly male) can easily be learned and replicated by AI models. When using recruitment AI, enterprises must be able to demonstrate that the model does not systematically discriminate against specific genders, ages, ethnicities, or disability statuses. It is recommended that enterprises conduct fairness audits before deploying recruitment AI, regularly monitor pass rate differences across groups, and maintain human review mechanisms to ensure every candidate receives fair treatment.
5.5 E-Commerce and Retail: New Transparency Requirements
E-commerce and retail AI applications (personalized recommendations, dynamic pricing, customer segmentation, AI customer service), while likely at lower risk levels than the aforementioned industries, still face non-negligible transparency requirements. Consumers have the right to know: is product recommendation ranking driven by AI algorithms rather than neutral evaluations? Does dynamic pricing produce differential pricing for different consumer groups? Are AI customer service responses auto-generated? Additionally, the "filter bubble" effect of recommendation systems and potential "price discrimination" in dynamic pricing may raise fairness concerns, and enterprises should establish self-monitoring mechanisms to ensure algorithmic behavior complies with fairness principles.
| Industry | Primary AI Applications | Expected Risk Level | Core Compliance Challenges | Competent Authority |
|---|---|---|---|---|
| Financial | Credit scoring, underwriting, AML, robo-advisory | High risk | Model explainability, fairness verification, data governance | FSC |
| Healthcare | Image diagnosis, medication recommendations, clinical decision support | High risk | Safety verification, human-AI collaboration processes, patient consent | Ministry of Health and Welfare |
| Manufacturing | Quality inspection, predictive maintenance, safety monitoring | Medium to High risk | Reliability verification, environmental robustness, safety reporting | MOEA / Ministry of Labor |
| Human Resources | Resume screening, interview assessment, performance prediction | High risk | Bias elimination, fairness audit, human review | Ministry of Labor |
| E-Commerce/Retail | Recommendation systems, dynamic pricing, AI customer service | Limited to Medium risk | Transparency labeling, pricing fairness, content disclosure | MODA / FTC |
6. Taiwan AI Basic Act vs. EU AI Act Comparison
For enterprises operating simultaneously in both the Taiwanese and European markets, understanding the differences and commonalities between the two laws is crucial. The following comparison table provides a systematic contrast across ten key dimensions[5][3]:
| Comparison Dimension | Taiwan AI Basic Act | EU AI Act |
|---|---|---|
| Legislative Level | Basic Act (principle-based framework legislation) | Regulation, directly applicable across the EU |
| Legislative Philosophy | Principle-based, supplemented by subsidiary legislation | Rule-based, detailed enumeration of requirements |
| Effective Date | Promulgated January 14, 2026 | Effective August 2024, phased implementation through 2027 |
| Competent Authority | NSTC coordination, ministerial division of labor | EU AI Office + national competent authorities |
| Risk Classification | Delegated to subsidiary legislation (expected three tiers) | Explicit four tiers: Prohibited/High/Limited/Minimal risk |
| Prohibited AI | No explicit prohibited list established | Explicitly prohibits social credit scoring, real-time remote biometric identification, etc. |
| High-Risk Compliance | To be specified by subsidiary legislation and industry guidelines | Detailed enumeration of technical documentation, risk management, data governance requirements |
| Penalties | No penalties in Basic Act; handled by sector laws | Up to 7% of global revenue or EUR 35 million |
| Extraterritorial Effect | In principle applies to domestic activities | Applies to all global enterprises serving the EU market |
| Generative AI | Included in definition scope; specific regulations pending | Dedicated chapter for GPAI models; additional obligations for systemic risk models |
As evident from the table above, Taiwan's AI Basic Act is currently less stringent than the EU AI Act in regulatory intensity, but this does not mean enterprises can be complacent. Three reasons: First, the Basic Act provides an authorization basis for subsequent subsidiary legislation, and ministerial industry guidelines may set stricter requirements than the EU AI Act in specific sectors; Second, enterprises operating in Taiwan while also exporting to the European market must still comply with the EU AI Act[5]; Third, the Basic Act's principle-based provisions may be cited by courts in judicial practice as standards for determining duty of care, and even without direct penalties, may affect the determination of civil liability.
7. Enterprise Response Timeline and Action Recommendations
Based on the Act's implementation timeline, anticipated subsidiary legislation release schedule, and enterprise internal preparation needs, we recommend the following phased response strategy[3]:
7.1 Immediate Action (2026 Q1): Inventory and Awareness Building
Initiate enterprise-wide AI system inventory. Led by the IT department with cooperation from legal, risk management, and business units, establish a complete AI system registry. The inventory scope should include not only self-developed AI systems but also third-party AI services in use (such as AI features embedded in SaaS products). Assemble an AI governance working group. Members should include representatives from legal, IT, risk management, HR, and key business departments, serving as the enterprise's standing coordination body for AI compliance. Conduct executive AI regulation briefing. Ensure the board of directors and C-level management understand the core requirements of the AI Basic Act and its implications for the enterprise.
7.2 Short-Term Planning (2026 Q2-Q3): Gap Analysis and Framework Construction
Complete AI compliance gap analysis. Cross-reference MODA's published high-risk classification guidelines with the enterprise AI system inventory results to identify compliance gaps. Establish AI governance policies and procedures. Including AI usage policy, AI risk assessment framework, AI impact assessment templates, and AI incident reporting procedures. Initiate compliance remediation for high-risk AI systems. For AI systems identified as high-risk, prioritize establishing transparency mechanisms, human oversight processes, and grievance channels.
7.3 Medium-Term Deepening (2026 Q4-2027 Q2): Institutional Implementation and Continuous Improvement
Embed AI governance into existing enterprise governance frameworks. Incorporate AI risks into the Enterprise Risk Management (ERM) framework, and include AI compliance in internal audit plans[10]. Establish AI model lifecycle management processes. Covering the complete governance workflow of model development, testing, deployment, monitoring, updating, and retirement. Conduct organization-wide AI literacy training. Targeting not only technical teams but also conducting compliance awareness education for business unit AI users. Participate in industry self-regulatory norm development. Through industry associations or consortiums, participate in industry-level AI self-regulatory code development, exercising influence during the standard-setting process.
7.4 Long-Term Vision (2027 Q3 onwards): Compliance Culture and Competitive Advantage
Transform AI governance from a compliance obligation into a competitive advantage. In supply chain partnerships, leading AI governance capabilities will become a differentiating factor for gaining international client trust. Establish regular AI governance maturity assessment mechanisms to continuously enhance enterprise AI governance capabilities. Track international AI regulatory developments (especially EU AI Act implementation practices) to ensure the enterprise's AI governance framework can adapt as the regulatory environment evolves[7].
8. Conclusion: From Compliance Pressure to Governance Capability
The enactment of the AI Basic Act marks a turning point for Taiwan's AI industry from "wild growth" to "orderly development"[1]. For enterprises, this is both a source of compliance pressure and an opportunity to build core competitiveness.
From a global perspective, the legalization of AI governance is an irreversible trend. The EU AI Act[5] has blazed the trail, and Taiwan, Japan, Korea, Singapore, and other Asia-Pacific economies are rapidly following suit. Enterprises that build AI governance capabilities early will gain advantages across three dimensions: First, first-mover advantage in regulatory compliance — while peers are scrambling to meet compliance requirements, enterprises with mature governance frameworks can respond with ease, and even convert compliance capability into commercial services. Second, entry tickets to international markets — an increasing number of international brand clients are incorporating AI governance into supply chain evaluation criteria, and suppliers lacking AI governance capabilities may be excluded from international supply chains. Third, a foundation of stakeholder trust — consumer, employee, investor, and public trust in AI is built on enterprises' ability to demonstrate responsible AI development and use.
Taiwan's AI Basic Act's principle-based legislative approach gives enterprises greater flexibility to design governance solutions suited to their own industry characteristics and organizational scale. But this flexibility also implies responsibility — enterprises cannot passively wait for competent authorities to tell them what to do, but should proactively build their own AI governance capabilities, participate in industry standard development, and view AI governance as organizational capacity building rather than a compliance burden[6].
From compliance pressure to governance capability, from passive response to proactive construction — this is the path every Taiwanese enterprise that takes AI seriously needs to walk. And the starting point of this path is right now.
Meta Intelligence's AI governance and compliance team is deeply engaged in research on both Taiwanese and international AI regulations, assisting enterprises from AI system inventory, risk classification assessment, and compliance gap analysis through to governance framework construction, providing end-to-end AI compliance implementation services. Regardless of what stage your enterprise is at in AI governance, we can tailor the most suitable response plan. Contact us now to let us help you transform the AI Basic Act's compliance requirements into your enterprise's competitive advantage.
